Data Set Information:

提供的文件包括三个不同的数据集。第一个包含所有24个超声波传感器的原始测量值和相应的类别标签。传感器读数的采样频率为每秒9个样本的速率。
第二个包含四个名为“简化距离”的传感器读数和相应的类别标签。这些简化的距离称为“前距离”、“左距离”、“右距离”和“后距离”。它们分别由位于机器人前部、左侧、右侧和后部60度弧内的传感器读数中的最小值组成。

第三个仅包含前面和左边的简化距离以及相应的类标签。

值得一提的是，24个超声波读数和简化的距离是在同一时间步采集的，因此每个文件具有相同的行数（每个采样时间步一行）。

“墙跟随”任务和数据收集旨在验证这样一个假设，即这个看似简单的导航任务确实是一个非线性可分离的分类任务。因此，线性分类器，如感知器网络，无法在不发生碰撞的情况下学习任务并指挥机器人在房间内走动。非线性神经分类器，如MLP网络，能够在不发生碰撞的情况下成功地学习任务和指挥机器人。
如果为神经分类器提供某种短期记忆机制，它们的性能通常会得到改善。例如，如果过去的输入与当前的传感器读数一起提供，则即使感知器也能够学习任务并成功地指挥机器人。如果使用递归神经网络（如Elman网络）来学习任务，则生成的动态分类器能够使用比MLP网络更少的隐藏神经元来学习任务。

建立了具有不同传感器读数数量的文件，以评估分类器在输入数量方面的性能。

Attribute Information:

Number of Attributes
  -- sensor_readings_24.data: 24 numeric attributes and the class.
  -- sensor_readings_4.data:   4 numeric attributes and the class.
  -- sensor_readings_2.data:   2 numeric attributes and the class.



For Each Attribute:
  -- File sensor_readings_24.data:
1. US1: ultrasound sensor at the front of the robot (reference angle: 180?°) - (numeric: real)
2. US2: ultrasound reading (reference angle: -165?°) - (numeric: real)
3. US3: ultrasound reading (reference angle: -150?°) - (numeric: real)
4. US4: ultrasound reading (reference angle: -135?°) - (numeric: real)
5. US5: ultrasound reading (reference angle: -120?°) - (numeric: real)
6. US6: ultrasound reading (reference angle: -105?°) - (numeric: real)
7. US7: ultrasound reading (reference angle: -90?°) - (numeric: real)
8. US8: ultrasound reading (reference angle: -75?°) - (numeric: real)
9. US9: ultrasound reading (reference angle: -60?°) - (numeric: real)
10. US10: ultrasound reading (reference angle: -45?°) - (numeric: real)
11. US11: ultrasound reading (reference angle: -30?°) - (numeric: real)
12. US12: ultrasound reading (reference angle: -15?°) - (numeric: real)
13. US13: reading of ultrasound sensor situated at the back of the robot (reference angle: 0?°) - (numeric: real)
14. US14: ultrasound reading (reference angle: 15?°) - (numeric: real)
15. US15: ultrasound reading (reference angle: 30?°) - (numeric: real)
16. US16: ultrasound reading (reference angle: 45?°) - (numeric: real)
17. US17: ultrasound reading (reference angle: 60?°) - (numeric: real)
18. US18: ultrasound reading (reference angle: 75?°) - (numeric: real)
19. US19: ultrasound reading (reference angle: 90?°) - (numeric: real)
20. US20: ultrasound reading (reference angle: 105?°) - (numeric: real)
21. US21: ultrasound reading (reference angle: 120?°) - (numeric: real)
22. US22: ultrasound reading (reference angle: 135?°) - (numeric: real)
23. US23: ultrasound reading (reference angle: 150?°) - (numeric: real)
24. US24: ultrasound reading (reference angle: 165?°) - (numeric: real)
  25. Class:
      -- Move-Forward
      -- Slight-Right-Turn
      -- Sharp-Right-Turn
      -- Slight-Left-Turn

  -- File sensor_readings_4.data:
1. SD_front: minimum sensor reading within a 60 degree arc located at the front of the robot - (numeric: real)
2. SD_left:  minimum sensor reading within a 60 degree arc located at the left of the robot  - (numeric: real)
3. SD_right: minimum sensor reading within a 60 degree arc located at the right of the robot - (numeric: real)
4. SD_back:  minimum sensor reading within a 60 degree arc located at the back of the robot - (numeric: real)
  5. Class:
      -- Move-Forward
      -- Slight-Right-Turn
      -- Sharp-Right-Turn
      -- Slight-Left-Turn

  -- File sensor_readings_2.data:
1. SD_front: minimum sensor reading within a 60 degree arc located at the front of the robot - (numeric: real)
2. SD_left:  minimum sensor reading within a 60 degree arc located at the left of the robot - (numeric: real)
  3. Class:
      -- Move-Forward
      -- Slight-Right-Turn
      -- Sharp-Right-Turn
      -- Slight-Left-Turn

Relevant Papers:

Ananda L. Freire, Guilherme A. Barreto, Marcus Veloso and Antonio T. Varela (2009),
'Short-Term Memory Mechanisms in Neural Network Learning of Robot Navigation
Tasks: A Case Study'. Proceedings of the 6th Latin American Robotics Symposium (LARS'2009),
Valpara?-so-Chile, pages 1-6, DOI: 10.1109/LARS.2009.5418323

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